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create_agent_chat_message

Send text messages in chat rooms with @mentions to route messages to specific recipients, supporting both name-based and ID-based recipient specification.

Instructions

Send a text message in a chat room.

Creates a new text message in a chat room. Messages MUST include at least
one @mention to ensure proper routing to recipients.

TWO WAYS TO SPECIFY RECIPIENTS:

Option 1 - Use `recipients` (recommended for LLMs):
    Provide comma-separated names. The tool resolves names to IDs automatically.
    Example: recipients="weather agent,sarah"

Option 2 - Use `mentions` (for libraries with caching):
    Provide a JSON array with pre-resolved IDs.
    Example: mentions='[{"id": "uuid-123", "name": "weather agent"}]'

If both are provided, `mentions` takes precedence (no API call needed).

For event-type messages (tool_call, tool_result, thought, error, etc.),
use create_agent_chat_event instead.

Args:
    chat_id: The unique identifier of the chat room (required).
    content: The message content/text (required).
    recipients: Comma-separated participant names to tag (LLM-friendly).
               Example: "weather agent,sarah,mike"
               Names are resolved to IDs via list_agent_chat_participants.
    mentions: JSON array of mentions with pre-resolved IDs (for libraries).
             Format: [{"id": "uuid", "name": "display_name"}, ...]
             When provided, skips name resolution (more efficient).

Returns:
    JSON string containing the created message details.

Examples:
    # LLM usage (names):
    create_agent_chat_message(chat_id="123", content="Hello!", recipients="weather agent")

    # Library usage (pre-resolved IDs):
    create_agent_chat_message(
        chat_id="123",
        content="Hello!",
        mentions='[{"id": "uuid-456", "name": "weather agent"}]'
    )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chat_idYes
contentYes
recipientsNo
mentionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: messages must include at least one @mention for routing, mentions parameter takes precedence over recipients when both provided, and name resolution occurs via list_agent_chat_participants. It doesn't cover error conditions or rate limits, but provides substantial operational context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, requirements, two recipient methods, alternatives, args, returns, examples). While comprehensive, some sentences could be more concise, but all content earns its place by adding value beyond the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (4 parameters, no annotations, 0% schema coverage), the description provides complete context: clear purpose, usage guidelines, parameter semantics, behavioral constraints, and examples. The presence of an output schema means the description doesn't need to explain return values in detail.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by explaining all 4 parameters in detail: chat_id and content are required, recipients is comma-separated names for LLMs, mentions is JSON array for libraries. It provides format examples, precedence rules, and resolution mechanisms that aren't in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Send a text message', 'Creates a new text message') and resource ('in a chat room'). It distinguishes itself from sibling create_agent_chat_event by specifying this is for text messages only, not event-type messages.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool vs. alternatives: it specifies to use create_agent_chat_event for event-type messages, and distinguishes between two recipient specification methods with clear recommendations for different use cases (LLMs vs. libraries with caching).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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